class_docstr: This class implements the task-specific Lingvo ASR model of Qin et al. (2019).
  
  The estimator uses a pre-trained model provided by Qin et al., which is trained using the Lingvo library and the
  LibriSpeech dataset.
  
  | Paper link: http://proceedings.mlr.press/v97/qin19a.html, https://arxiv.org/abs/1902.08295
  
  .. warning:: In order to calculate loss gradients, this estimator requires a user-patched Lingvo module. A patched
               source file for the `lingvo.tasks.asr.decoder` module will be automatically applied. The original
               source file can be found in `<PYTHON_SITE_PACKAGES>/lingvo/tasks/asr/decoder.py` and will be patched as
               outlined in the following commit diff:
               https://github.com/yaq007/lingvo/commit/414e035b2c60372de732c9d67db14d1003be6dd6
  
  The patched `decoder_patched.py` can be found in `ART_DATA_PATH/lingvo/asr`.
  
  Note: Run `python -m site` to obtain a list of possible candidates where to find the `<PYTHON_SITE_PACKAGES` folder.
docstr: Perform batch-wise prediction for given inputs.
  
  :param x: Samples of shape `(nb_samples)` with values in range `[-32768, 32767]`. Note that it is allowable
            that sequences in the batch could have different lengths. A possible example of `x` could be:
            `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
  :param batch_size: Size of batches.
  :return: Array of predicted transcriptions of shape `(nb_samples)`. A possible example of a transcription
           return is `np.array(['SIXTY ONE', 'HELLO'])`.
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code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
      """
      Perform batch-wise prediction for given inputs.
  
      :param x: Samples of shape `(nb_samples)` with values in range `[-32768, 32767]`. Note that it is allowable
                that sequences in the batch could have different lengths. A possible example of `x` could be:
                `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
      :param batch_size: Size of batches.
      :return: Array of predicted transcriptions of shape `(nb_samples)`. A possible example of a transcription
               return is `np.array(['SIXTY ONE', 'HELLO'])`.
      """
      if x[0].ndim != 1:  # pragma: no cover
          raise ValueError(
              "The LingvoASR estimator can only be used temporal data of type mono. Please remove any channel"
              "dimension."
          )
      # if inputs have 32-bit floating point wav format, the preprocessing argument is required
      is_normalized = max(map(max, np.abs(x))) <= 1.0  # type: ignore
      if (
          is_normalized and self.preprocessing is None
      ):  # pragma: no cover
          raise ValueError(
              "The LingvoASR estimator requires input values in the range [-32768, 32767] or normalized input values"
              " with correct preprocessing argument (mean=0, stddev=1/normalization_factor)."
          )
  
      nb_samples = x.shape[0]
      assert (
          nb_samples % batch_size == 0
      ), "Number of samples must be divisible by batch_size"
  
      # Apply preprocessing
      x, _ = self._apply_preprocessing(x, y=None, fit=False)
  
      y = []
      nb_batches = int(np.ceil(nb_samples / float(batch_size)))
      for m in range(nb_batches):
          # batch indices
          begin, end = m * batch_size, min(
              (m + 1) * batch_size, nb_samples
          )
  
          x_batch_padded, _, mask_frequency = self._pad_audio_input(
              x[begin:end]
          )
  
          feed_dict = {
              self._x_padded: x_batch_padded,
              self._y_target: np.array(["DUMMY"] * batch_size),
              self._mask_frequency: mask_frequency,
          }
          # run prediction
          y_batch = self.sess.run(self._predict_batch_op, feed_dict)
  
          # extract and append transcription result
          y += y_batch["topk_decoded"][:, 0].tolist()
  
      y_decoded = [item.decode("utf-8").upper() for item in y]
      return np.array(y_decoded, dtype=str)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import sys
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  import numpy
  from art import config
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
  from art.estimators.tensorflow import TensorFlowV2Estimator
  from art.utils import get_file, make_directory
label_desc: Use image node_id 10 for calling the TensorFlowLingvoASR obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: np